Advanced Signal Processing Notebooks and Tutorials

Overview

Advanced Digital Signal Processing
Notebooks and Tutorials

Prof. Dr. -Ing. Gerald Schuller
Jupyter Notebooks and Videos: Renato Profeta

Applied Media Systems Group
Technische Universität Ilmenau

Content

  • 01 Quantization:
    NBViewerBinderGoogle ColabYoutube

    • Introduction
    • Quantization Error
    • Uniform Quantizers: Mir-Rise and Mid-Tread
    • Python Example: Uniform Quantizers
    • Python Example: Real-time Quantization Example
  • 02 Quantization - Signal to Noise Ratio (SNR):
    NBViewerBinderGoogle ColabYoutube

    • Signal to Noise Ratio (SNR)
    • SNR for Uniformly Distribution Signals
    • SNR for a Sine Wave
      • PDF of Time Series
  • 03 Quantization - Non-Uniform Quantization:
    NBViewerBinderGoogle ColabYoutube

    • Companding
      • µ-LAw and A-Law
      • Python Example: µ-LAw
      • Python Example: Real-Time Mid-Tread, Mid-Rise, µ-Law
  • 04r Quantization - Revision: Histogram, PDFs, Numerical Integration
    NBViewerBinderGoogle ColabYoutube

    • Histograms
    • Probability Density Functions
    • Numerical Integration
      • Riemann Sum
      • Trapezoidal Rule
  • 04 Quantization - Lloyd-Max Quantizer
    NBViewerBinderGoogle ColabYoutube

    • Lloyd-Max Quantizer
    • Lloyd-Max Quantizer Examples
  • 05 Quantization - Vector Quantizer (VQ) and Linde-Buzo-Gray (LBG) Algorithm
    NBViewerBinderGoogle ColabYoutube

    • Vector Quantization
    • Linde-Buzo-Gray Algorithm
    • Python Examples: Vector Quantization in an Encoder and Decoder
      • Iron Maiden - The Number of the Beast Introduction
      • Iron Maiden - Aces High Introduction
  • 06 Sampling - Sampling a Discrete Time Signal
    NBViewerBinderGoogle ColabYoutube

    • Sampling Introduction
    • Sampling a Discrete Time Signal
      • Downsampling
      • Upsampling
    • Python Example: Live Spectrogram: Sampling, LP Filtering
  • 07a The z-Transform - Theory and Properties
    NBViewerBinderGoogle ColabYoutube

    • The z-Transform Definition
    • Properties of the z-Transform
      • Shift Property
      • Linearity
      • Convolution
    • z-Transform Example: Exponential Decaying Sequence
  • 07b Filters - FIR and IIR Filters
    NBViewerBinderGoogle ColabYoutube

    • Filters: Linear Time-Invariant Systems
    • Finite Impulse Response (FIR) Filters
    • Infinite Impulse Response (IIR) Filters
    • Filter Example: Exponential Decaying Signal
      • Computing the Resulting Frequency Response
      • The z-Plane
      • Impulse Response
  • 08 Filters and Noble Identities
    NBViewerBinderGoogle ColabYoutube

    • Filter Design
      • Linear Phase and Signal Delay
      • General Phase and Groud Delay
      • Magnitude
    • Multirate Noble Identities
    • Polyphase Vectors
    • Python Example: Noble Identities and Polyphase Vectors
  • 09 Allpass Filters and Frequency Warping
    NBViewerBinderGoogle ColabYoutube

    • Allpass Filters
      • Allpass Filter as Fractional Delay
      • IIR Fractional Delay Filter Design
      • Simple IIR Allpass Filters
    • Frequency Warping Introduction
    • Frequency Warping and Bark Scale
  • 10 Frequency Warping and Minimum Phase Filters
    NBViewerBinderGoogle ColabYoutube

    • Frequency Warping
    • Minimum Phase Filters
      • Python Example
      • Impulse Response
      • Frequency Response
  • 11 Complex Signals and Filters, Hilbert Transform
    NBViewerBinderGoogle ColabYoutube

    • Complex Signals and Filters
    • Hilbert Transformer
      • Python Example
      • Impulse Response
      • Frequency Response
    • Example for the Measurement of the (Instantaneous) Amplitude
  • 12 Wiener Filters
    NBViewerBinderGoogle ColabYoutube

    • Wiener Filters
      • Python Example for Denoising Speech
      • Scipy Wiener Filter Example: Iron Maiden - The Number of the Beast Speech Intro
  • 13 Matched Filters
    NBViewerBinderGoogle ColabYoutube

    • Matched Filters
      • Python Example: Closed Form Solution
      • Convolutional Neural Network Implementation: PyTorch
  • 14 Prediction
    NBViewerBinderGoogle ColabYoutube

    • Prediction
      • Wiener-Hopf Closed Form Solution
      • Encoder-Decoder System
      • Neural Network Implementation - PyTorch
    • Linear Predictive Coding (LPC)
    • Least Mean Squares (LMS) Algorithm
      • LMS with Quantizer

YouTube Playlist

Youtube

Requirements

Please check the following files at the 'binder' folder:

  • environment.yml
  • postBuild

Note

Examples requiring a microphone will not work on remote environments such as Binder and Google Colab.

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